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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/6167

Title: System Identification Methods For Reverse Engineering Gene Regulatory Networks
Authors: WANG, ZHEN

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Keywords: system identification
reverse engineering
gene regulatory networks
Issue Date: 2010
Series/Report no.: Canadian theses
Abstract: With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches, a temporal synthetic dataset is developed. Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. Furthermore, the methods are also applied to a biological dataset in yeast Saccharomyces Cerevisiae. This dataset includes 14 cell-cycle regulated genes; their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. Resulting networks are also compared with two previous studies in the literature on the same dataset.
Description: Thesis (Master, Computing) -- Queen's University, 2010-10-18 20:47:36.458
URI: http://hdl.handle.net/1974/6167
Appears in Collections:Queen's Graduate Theses and Dissertations
School of Computing Graduate Theses

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